Automated vending machine with customer and identification authentication

ABSTRACT

Implementations include actions of receiving consumer-specific data and ID-specific data from an identification presented by a consumer to a vending machine, processing at least a portion of the ID-specific data to determine one or more of whether the identification is unexpired and whether the identification is authentic, and serving the consumer from the vending machine at least partially in response to determining that the identification is unexpired and that the identification is authentic and determining that the consumer is authentic relative to the identification.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation U.S. Ser. No. 16/785,872, filed onFeb. 10, 2020, and entitled AUTOMATED VENDING MACHINE WITH CUSTOMER ANDIDENTIFICATION AUTHENTICATION, which claims the benefit of and priorityto U.S. 62/865,420, filed on Jun. 24, 2019, and entitled AUTOMATEDVENDING MACHINE WITH CUSTOMER AND IDENTIFICATION AUTHENTICATION, thedisclosure of which are expressly incorporated herein by reference intheir entirety.

BACKGROUND

Vending machines enable provisioning of goods and/or services tocustomers. In some instances, goods and/or services requested bycustomers can be restricted. For example, a customer may be required tobe of a minimum age and/or have appropriate licensing, credentials, orother approval (e.g., prescription) to obtain particular goods and/orservices. Using alcohol as a non-limiting example, a customer may berequired to be at least 21 years of age to purchase and consumealcoholic beverages. Using tobacco as another non-limiting example, acustomer may be required to be at least 18 years of age to purchasetobacco products. Using rental vehicles as still another non-limitingexample, access to a rental vehicle can be limited to customers having avalid driver license and being of at least a certain age (e.g., 18 yearsold, 24 years old).

While vending machines provide numerous advantages, provisioning ofrestricted goods and/or services requires particular functionality inorder to be fully-automated. For example, the vending machine itself isrequired to authenticate documentation provided by the customer (e.g.,license, identification, prescription) as well as the customer (e.g.,verify that the customer requesting the goods/services matches theperson associated with the documentation). Although systems have beendeveloped to perform such functionality, traditional systems can sufferfrom disadvantages. Example disadvantages is inconsistently discerningbetween authentic documentation and forged documentation, and notaccurately determining that the customer requesting the goods/servicesmatches the person associated with the documentation.

SUMMARY

Implementations of the present disclosure are generally directed tovending machines. More particularly, implementations of the presentdisclosure are directed to a vending machine that verifiescharacteristics (e.g., an age of a customer, an identity of a customer)before dispensing a product.

In some implementations, actions include receiving consumer-specificdata and ID-specific data from an identification presented by a consumerto a vending machine, processing at least a portion of the ID-specificdata to determine one or more of whether the identification is unexpiredand whether the identification is authentic, and serving the consumerfrom the vending machine at least partially in response to determiningthat the identification is unexpired and that the identification isauthentic and determining that the consumer is authentic relative to theidentification. Other implementations of this aspect includecorresponding systems, apparatus, and computer programs, configured toperform the actions of the methods, encoded on computer storage devices.

These and other implementations can each optionally include one or moreof the following features: determining whether the identification isauthentic includes comparing features of the identification to knownfeatures of a type of the identification; determining whether theidentification is authentic includes providing an image of theidentification to a machine learning (ML) model and receiving anindication from the ML model indicating whether the identification isauthentic; determining whether the consumer is authentic relative to theidentification includes providing image data and/or video data depictingthe consumer and an image from the identification to a facialrecognition system that processes the data to determine whether theconsumer matches the image provided with the identification; actionsfurther include determining a state of the consumer, serving theconsumer being further in response to the state of the user; the stateincludes sober; actions further include determining that a person isphysically present at the vending machine, serving the consumer beingfurther in response to determining that a person is physically presentat the vending machine; and determining that a person is physicallypresent is at least partially based on a depth determined using one ormore images generated by at least one camera of the vending machine.

The present disclosure also provides a computer-readable storage mediumcoupled to one or more processors and having instructions stored thereonwhich, when executed by the one or more processors, cause the one ormore processors to perform operations in accordance with implementationsof the methods provided herein.

The present disclosure further provides a system for implementing themethods provided herein. The system includes one or more processors, anda computer-readable storage medium coupled to the one or more processorshaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform operationsin accordance with implementations of the methods provided herein.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also include any combination of the aspects andfeatures provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIGS. 1A and 1B depict a beverage vending machine in accordance withimplementations of the present disclosure.

FIGS. 2A and 2B depict example architectures in accordance withimplementations of the present disclosure.

FIG. 3 depicts an example conceptual diagram in accordance withimplementations of the present disclosure.

FIGS. 4A-4F depict example user interfaces in accordance withimplementations of the present disclosure.

FIG. 5 depicts an example process in accordance with implementations ofthe present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Implementations of the present disclosure are generally directed tovending machines. More particularly, implementations of the presentdisclosure are directed to a vending machine that verifiescharacteristics of a customer (e.g., an age of a customer, an identityof a customer) before dispensing a product. Implementations includeactions of receiving consumer-specific data and ID-specific data from anidentification presented by a consumer to a vending machine, processingat least a portion of the ID-specific data to determine one or more ofwhether the identification is unexpired and whether the identificationis authentic, and serving the consumer from the vending machine at leastpartially in response to determining that the identification isunexpired and that the identification is authentic and determining thatthe consumer is authentic relative to the identification.

Implementations of the present disclosure are described in furtherdetail herein with reference to an example vending machine. The examplevending machine includes a vending machine that dispenses restrictedproducts (e.g., age restricted). In the example described herein, thevending machine is a beverage vending machine that dispenses alcoholicbeverages (e.g., beverages restricted to persons aged 21 and above). Itis contemplated, however, that implementations of the present disclosurecan be realized with any appropriate vending machines that dispenserestricted goods and/or services. For example, and without limitation,vending machines in accordance with implementations of the presentdisclosure can be used to provision rental cars, cannabis, ammunition,medications, airline tickets, utility services, and the like to persons.Further, implementations of the present disclosure can be realized basedon any appropriate restriction. For example, implementations of thepresent disclosure are described herein with reference to agerestriction. Other example restrictions can include, without limitation,licensing restriction (e.g., consumer must have and present validlicense relevant to particular goods/services), and prescription (e.g.,consumer must have and present a valid prescription from a licensedhealthcare practitioner).

FIGS. 1A and 1B depict a beverage vending machine 100 in accordance withimplementations of the present disclosure. The vending machine 100includes a housing 102, an interface 104, and a beverage dispenser 106.As described in further detail herein, the housing 102 houses devices,systems, and beverages that can be dispensed to consumers through thebeverage dispenser. For example, the housing 102 can house, withoutlimitation, one or more computing devices, a refrigeration system, oneor more beverage reservoirs (e.g., kegs, boxes, bottles), and a beveragedispensing system (e.g., lines, taps, pressure source).

In the example of FIGS. 1A and 1B, the interface 104 includes a displayscreen 108 and one or more cameras 110. In some implementations, thedisplay screen 108 displays one or more user interfaces (UIs) thatenable a consumer to interact with the vending machine 100. In someexamples, the display screen 108 is provided as a touchscreen thatdisplays one or more UIs and that is responsive to user input (e.g., theconsumer touching the display screen 108). In this manner, the consumercan provide touch input to the vending machine 100 through the displayscreen 108 to, among other things, make a selection, input information,and review beverage options. In some examples, each of the one or morecameras 110 is a digital camera that generates digital images. Althoughmultiple cameras 110 are depicted in the example of FIGS. 1A and 1B,implementations of the vending machine 100 can be realized with a singlecamera 110. In some examples, a camera 110 can include, withoutlimitation, a still camera, a video camera, an infra-red (IR) camera, orany appropriate camera. In some examples, the single camera 110 includescombined capabilities (e.g., combined still, video, and IR camera). Insome examples, multiple cameras 110 are provided. For example, a firstcamera 110 can include first capabilities (e.g., still, video), and asecond camera 110 can include second capabilities (e.g., IR). In someexamples, data generated using multiple cameras can be used to providestereoscopic imaging and/or video (e.g., multi-dimensional).

Although not depicted in FIGS. 1A and 1B, the vending machine 100 caninclude one or more microphones for generating audio data. In someexamples, the interface 104 can include one or more microphones. Forexample, at least one camera 110 of the one or more cameras 110 caninclude a microphone.

In the example of FIGS. 1A and 1B, the vending machine 100 includes anidentification (ID) scanner 112 and a card reader 114. In some examples,the ID scanner 112 scans a form of identification (e.g., a residencecard, a driver's license, a passport) that records consumer-specificinformation. Example consumer-specific information can include, withoutlimitation, one or more identification images (e.g., facial image,fingerprint image), name, address, date-of-birth (DOB), age, address,unique identifier (e.g., resident number, license number, passportnumber), and gender. In some examples, the identification records theconsumer-specific information in analog form (e.g., printed on theidentification) and/or digital form (e.g., digitally recorded in memoryon the identification). In some examples, the ID scanner 112 scans theidentification to determine at least a portion of the consumer-specificinformation. For example, the ID scanner 112 can record an image of theidentification and can process the image (e.g., using optical characterrecognition, and/or image recognition) to determine one or more of theidentification image(s), the name, the address, the DOB, the age, theaddress, the unique identifier, and the gender recorded on theidentification. As another example, the ID scanner 112 can read a memoryof the identification to retrieve one or more of the identificationimage(s), the name, the address, the DOB, the age, the address, theunique identifier, and the gender recorded on the identification.

In some examples, the card reader 114 reads payment information to remitpayment for a beverage that is to be served by the vending machine 100.In some examples, the card reader 114 is a traditional card reader thatingests a payment card (e.g., credit card, debit card, gift card) havingpayment information recorded thereon. Payment is facilitated asdescribed in further detail herein. Although a card reader 114 isprovided, it is contemplated that the vending machine 100 can use anyappropriate payment technique. Example payment techniques include,without limitation, card payment (e.g., credit card, debit card, giftcard), near-field communication (NFC) payment (e.g., touch-to-pay), andmachine-readable code payment (e.g., bar code, QR code scanning). Insome examples, the vending machine 100 enables consumers to select apayment technique from a set of payment techniques. In some examples,the card reader 114 is not included in the vending machine 100 (e.g.,the vending machine 100 accepts only non-card payments (e.g., NFCpayment, machine-readable code payment). In some examples, the vendingmachine 100 enables use of government issued currency and/or currencyrepresentation (e.g., tokens, coupons) as payment options.

In the example of FIGS. 1A and 1B, the beverage dispenser 106 includesan opening 116 for receiving a vessel 118. In some examples, the opening106 enables access to one or more taps that dispense a beverage into thevessel 118. In the depicted example, the vessel 118 is provided as aglass. It is contemplated, however, that any appropriate vessel can beused for receiving a dispensed beverage.

FIG. 2A depicts an example architecture 200 in accordance withimplementations of the present disclosure. The example architecture 200includes a vending machine 202 (e.g., the vending machine 100 of FIGS.1A and 1B), a network 204, an administrator system 206, a paymentprocessing system 208, and a facial recognition system 210. In someexamples, the network 204 is provided as a large computer network, suchas a local area network (LAN), a wide area network (WAN), the Internet,a cellular network, a telephone network (e.g., PSTN) or an appropriatecombination thereof connecting any number of vending machines 202,communication devices, mobile computing devices, fixed computing devicesand server systems.

In some examples, each of the administrator system 206, the paymentprocessing system 208, and the facial recognition system 210 is hostedon one or more servers and are accessible by the vending machine 202over the network 204. In some examples, the administrator system 206 isoperated by or on behalf of the enterprise that operates the vendingmachine 202. For example, the enterprise 206 can remotely interact withthe vending machine 202 through the administrator system 206 to performone or more functionalities of a plurality of functionalities (e.g.,update software, set configuration parameters, perform diagnostics,receive sales data, receive consumption data), as described in furtherdetail herein. In some examples, the payment processing system 208 isoperated by a third-party payment processing service, as described infurther detail herein. In some examples, the facial recognition system210 is operated by a third-party facial recognition service, asdescribed in further detail herein.

In the example of FIG. 2A, the vending machine 202 includes a computingdevice 220, one or more cameras 222 (e.g., the cameras 110 of FIG. 1), acard reader 224 (e.g., the card reader 114 of FIG. 1), an ID scanner 226(e.g., the ID scanner 112 of FIG. 1), a beverage dispensing system 228,and in input/output (I/O) acquisition system 230 (e.g., LabJack T7).

In some examples, the computing device 220 includes any appropriate typeof computing device such as, and without limitation, a desktop computer,a laptop computer, a handheld computer, and a tablet computer. Forexample, the computing device 220 can be provided as an industrialpersonal computer (PC) that executes an operating system (e.g., Windows10 provided by Microsoft Corp.). The computing device 220 executes akiosk application 240, a payment application 242, and a scanner service244. In some examples, each of the kiosk application 240, the paymentapplication 242, and the scanner service 244 is provided as one or morecomputer-executable programs executed by the computing device 220. Insome examples, the computing device 220 is in communication (e.g.,wired, wireless) with the one or more cameras 222 to receive image dataand/or audio data (e.g., the kiosk application 240 ingests at least aportion of image data and/or audio data provided from the one or morecameras).

In some examples, the computing device 220 is in communication (e.g.,wired, wireless) with the beverage dispensing system 228. In thedepicted example, the computing device 220 is in communication with thebeverage dispensing system 228 through the I/O acquisition system 230.In some examples, the kiosk application 240 issues commands to and/orreceives data from the beverage dispensing system 228. For example, thekiosk application 240 can issue commands to the beverage dispensingsystem 228 to dispense a particular beverage selected by a consumer. Asanother example, the kiosk application 240 can receive data from thebeverage dispensing system 228, the data indicating a status of thebeverage dispensing system 228 and/or one or more beverages within thevending machine 202. Example data can include, without limitation,beverage quantity, beverage temperature, hardware faults, softwarefaults.

In some examples, the computing device 220 is in communication (e.g.,wired, wireless) with each of the card reader 224 and the ID scanner. Insome examples, the card reader 224 reads payment information (e.g., froma credit/debit/gift card) and provides the payment information to thepayment application. In some examples, the payment application 242provides at least a portion of the payment information to the paymentprocessing system 208, which processes the at least a portion of thepayment information to provide a payment approval or payment denialdecision (e.g., the payment submitted by the consumer has beenapproved/denied). In some examples, the payment application 242 receivesthe payment decision from the payment processing system 208 and providesthe payment decision to the kiosk application 240, which determineswhether to serve a beverage to the consumer at least partially based onthe payment decision (e.g., if the payment is approved and the consumeris authenticated and is of the required age, serve the beverage; if thepayment is approved, but the consumer is either not authenticated or isnot of the required age, do not serve the beverage; if the payment isnot approved, do not serve the beverage).

In some examples, the ID scanner 226 scans an identification presentedby a consumer to determine at least a portion of the consumer-specificinformation. For example, the ID scanner 226 can record an image of theidentification and can process the image (e.g., using optical characterrecognition, and/or image recognition) to determine one or more of theidentification image(s), the name, the address, the DOB, the age, theaddress, the unique identifier, and the gender recorded on theidentification. As another example, the ID scanner 226 can read a memoryof the identification to retrieve one or more of the identificationimage(s), the name, the address, the DOB, the age, the address, theunique identifier, and the gender recorded on the identification.

In some implementations, the ID scanner 226 reads a barcode (and/or QRcode), magnetic strip, and takes full front and back images of theidentification for OCR. In some examples, the scanner service 244validates the identification by cross-referencing the data and checkingfor security. In some implementations, the kiosk application 240 readsonly the information necessary to check the results of the IDvalidation, validate the consumer's DOB, and first name for apersonalized experience (e.g., display the user's name in the UI). Insome examples, no personally identifiable information (PII) is stored inthe vending machine 202. In some examples, the only personal consumerinformation that is transmitted from the vending machine 202 is theimage from the identification and the image, video, and/or audio that iscaptured by the vending machine 202 (e.g., sent to the facialrecognition service 210). However, this data is not persisted at eitherthe vending machine 202 or by the facial recognition service 210.

In some implementations, and as described in further detail herein, thekiosk application 240 receives data representative of a consumerrequesting beverage service and determines whether to serve the consumerat least partially based on the data. In some implementations, the kioskapplication 240 receives at least a portion of the consumer-specificdata provided from the identification, as described herein. Exampleconsumer-specific information can include, without limitation, imagedata (e.g., picture provide with the identification), gender, height,age, name, and address. In some examples, the kiosk application 204receives at least a portion of ID-specific data provided for theidentification. Example ID-specific data can include, withoutlimitation, one or more patterns, an expiration date, and one or morewatermarks (e.g., visible, invisible, ultra-violet (UV)). In someexamples, the kiosk application 240 receives current consumer datarepresentative of the consumer, who is present at the vending machine202 and is requesting service. Example current consumer data caninclude, without limitation, image data, video data, and audio data(e.g., provided from the one or more cameras 222).

In accordance with implementations of the present disclosure, the kioskapplication 240 determines whether the identification is expired. Insome examples, determining whether the identification is expired can beexecuted by processing at least a portion of the ID-specific data. Forexample, the kiosk application 240 can compare an expiration date of theidentification to a current date to determine whether the identificationhas expired. If the identification is expired, no further processing isrequired, as it can be determined that the consumer cannot be servedwithout an unexpired identification. If the identification is notexpired, further processing can be performed to determine whether theconsumer can be served.

In some implementations, the kiosk application 240 determines whetherthe age indicated on the identification is of a legal age forconsumption of the beverage. For example, the kiosk application 240 cancompare the age indicated on the identification (e.g., by comparing acurrent date to the DOB provided on the identification) to a legal age(e.g., a statutorily-defined legal age for the particular location). Ifthe age is not of the legal age, no further processing is required, asit can be determined that the consumer cannot be served. If the age isof the legal age, further processing can be performed to determinewhether the consumer can be served.

In some implementations, the kiosk application 240 determines whetherthe presented identification is authentic. That is, the kioskapplication 240 determines whether the identification is real, aforgery, or damaged such that authenticity cannot be determined. If itis determined that the identification is not authentic, no furtherprocessing is required, as it can be determined that the consumer cannotbe served without an authentic identification. If it is determined thatthe identification is authentic, further processing can be performed todetermine whether the consumer can be served.

In some examples, the kiosk application 240 can process at least aportion of the ID-specific data to determine whether the identificationis authentic. In some examples, features of the presented identificationcan be compared to known features of the particular type ofidentification. Example features can include, without limitation, one ormore patterns, one or more watermarks, one or more images, and locationsof each on the identification. Example features can also include,without limitation, locations and/or format of text depicted on theidentification.

In some examples, features of the presented identification can becompared to known features based on a set of rules, each rule definingwhether a feature conforms to a respective known feature. In someexamples, multiple sets of rules can be provided, each rulecorresponding to a respective type (e.g., residence card, driver'slicense, passport) and/or issuing authority (e.g., city, state, nationalgovernment) of the identification. For example, it can be determinedthat the identification is a driver's license from the State of Texas(e.g., text on the license can be processed using optical characterrecognition (OCR) to determine a type of identification and the issuingauthority; an image of the identification can be processed through amachine learning (ML) model that is trained to classify identificationsinto types and/or issuing authorities based on images of theidentifications; that is, the ML model outputs a type and an authority).In response to determining that the identification is a driver's licensefrom the State of Texas, a set of rules corresponding to Texas driver'slicenses can be used.

In some examples, the image of the identification can be processedthrough a ML model that is trained to classify identifications asauthentic or as inauthentic. For example, the ML model can be trainedbased on known, authentic identifications to be able to discern betweeninauthentic identifications and authentic identifications. In someexamples, the ML model outputs one or more classifications, eachclassification having a confidence score. For example, the ML model canoutput a classification of authentic with a confidence score of 0.98.The confidence score represents a relative confidence in the accuracy ofthe classification. In some examples, the confidence score can becompared to a threshold confidence score. If the confidence scoreexceeds the threshold confidence score, the classification is determinedto be correct. If the confidence score does not exceed the thresholdconfidence score, the classification is determined to be incorrect. Ifthe classification is determined to be correct, the identification is,in the example above, determined to be authentic. If the classificationis determined to be incorrect, the identification is, in the exampleabove, determined to be inauthentic.

In some implementations, at least a portion of the current consumer datacan be processed to determine whether the consumer, who presented theidentification is authentic. That is, it is determined whether theconsumer, who presented the identification is the person represented bythe identification (e.g., to determine whether the consumer is usingsomeone else's identification). For example, current consumer data caninclude image data and/or video data depicting the consumer that ispresent at the vending machine 202. In some examples, the image dataand/or the video data can be processed to determine whether the consumermatches the image provided with the identification. For example, theimage data and/or the video data, and the image from the identificationcan be provided to the facial recognition system 210, which can processthe data to determine whether the consumer matches the image providedwith the identification. If the consumer does not match the imageprovided from the identification, it can be determined that the consumeris not authentic and cannot be served. If the consumer does match theimage provided from the identification, it can be determined that theconsumer is authentic and can be served.

For example, the facial recognition system 210 can employ one or morefacial recognition models (e.g., ML models) that can compare images todetermine whether faces depicted in the images match. In some examples,the facial recognition system 210 receives the image from theidentification and the image from the vending machine, each imagedepicting a face. The facial recognition system 210 processes the imagesto determine whether the faces are the same. For example, a ML model canprocess the images and outputs one or more classifications, eachclassification having a confidence score. For example, the ML model canoutput a classification of match with a confidence score of 0.98. Theconfidence score represents a relative confidence in the accuracy of theclassification. In some examples, the confidence score can be comparedto a threshold confidence score. If the confidence score exceeds thethreshold confidence score, the classification is determined to becorrect. If the confidence score does not exceed the thresholdconfidence score, the classification is determined to be incorrect. Ifthe classification is determined to be correct, the faces depicted inthe images, in the example above, are determined to match. If theclassification is determined to be incorrect, the faces depicted in theimages, in the example above, are determined to not match.

In some implementations, the image data and/or the video data can beprocessed to determine demographic features of the consumer. Exampledemographic features can include, without limitation, age, gender, andheight. For example, the image data and/or the video data can beprovided to a demographic feature system, which can process the data todetermine demographic features of the consumer. In some examples, thedemographic features can be compared to respective demographic featuresof the consumer-specific information provided from the identification.In some examples, if at least a portion of the demographic features ofthe consumer do not match the respective demographic features of theconsumer-specific information provided from the identification, and/orthe consumer does not match the image provided from the identification,it can be determined that the consumer is not authentic and cannot beserved.

For example, the demographic feature system (e.g., which can be a systemalso provided with the facial recognition system 210) can employ one ormore demographic feature recognition models (e.g., ML models) that cananalyze images to determine demographic features of a person depictedtherein. In some examples, the demographic feature system receives theimage from the vending machine (an image generated by the vendingmachine) and processes the image to determine one or more demographicfeatures. For example, a ML model can process the image and outputs oneor more classifications, each classification having a confidence score.For example, the ML model can output a classification of an age with aconfidence score of 0.98. The confidence score represents a relativeconfidence in the accuracy of the classification. In some examples, theconfidence score can be compared to a threshold confidence score. If theconfidence score exceeds the threshold confidence score, theclassification is determined to be correct. If the confidence score doesnot exceed the threshold confidence score, the classification isdetermined to be incorrect. If the classification is determined to becorrect, the demographic feature (e.g., age) determined from the image,in the example above, is determined to be correct. If the classificationis determined to be incorrect, the demographic feature (e.g., age)determined from the image, in the example above, is determined to beincorrect.

In some implementations, one or more other conditions can be evaluatedto determine whether the consumer is to be served. Example otherconditions can include, without limitation, a service history for theconsumer, a state of the user, venue constraints, and legal constraints.

In some examples, the service history for the consumer includes ahistory of beverages served to the user within a particular time period.For example, if the consumer has been served a threshold number ofbeverages (e.g., X beverages) within a threshold time period (e.g., Yminutes), it can be determined that the user is not to be served. Inthis manner, over-serving of the consumer can be avoided, and/or theconsumer purchasing beverages for others can be mitigated.

In some implementations, the service history is determined based on dataprovided from the vending machine 202. That is, for each consumer, thevending machine 202 can record each transaction (e.g., each time theconsumer purchases a beverage). In some examples, a transaction recordis provided that includes a time, a date, a location (e.g., uniqueidentifier of the vending machine 202), a beverage purchased, and thelike. In some examples, a consumer record can be provided that includesa set of transaction records.

In some implementations, the service history is determined at leastpartially based on data provided from other vending machines 202. Forexample, a set of vending machines 202 can be located within a facility(e.g., a stadium), and consumers can purchase beverages from multiplevending machines 202. In some examples, a consumer record includestransactions from any vending machine 202 within the facility. In someexamples, the consumer records can be stored on each of the vendingmachines 202. In some examples, the consumer records can be stored in acentral repository that each vending machine 202 can access to determinewhether a particular consumer is to be served.

In some implementations, the service history is determined at leastpartially based on additional data representative of transactions. Insome examples, the additional data can represent transactions thatoccurred remotely from (or otherwise external to) the facility, at whichthe vending machine 202 is located. For example, the computing device220 can receive additional data indicating that the consumer purchasedalcoholic one or more alcoholic beverages at a bar and/or restaurantprior to a currently requested purchase at the vending machine 202.

In some implementations, a state of the consumer can be determined andcan be compared to one or more known states. In some examples, the stateof the consumer can represent a behavior of the consumer at the vendingmachine. For example, image data and/or video data can be processed todetermine one or more characteristics that are representative of a stateof the consumer. Example characteristics can include, withoutlimitation, bloodshot eyes, slurred speech, motion, and the like. Insome examples, the one or more characteristics can be determined for theconsumer and can be used to provide a consumer profile. In someexamples, the consumer profile is evaluated to determine a state of theuser. Example states can include, without limitation, sober, tipsy,drunk. In some examples, service can be denied based on state. Forexample, if it is determined that the state of the consumer is drunk,service to the consumer can be declined.

For example, a state recognition system (e.g., which can be a systemalso provided with the facial recognition system 210) can employ one ormore state recognition models (e.g., ML models) that can analyze images,video, and/or audio to determine a state of a person depicted therein.In some examples, the state recognition system receives the images,video, and/or audio from the vending machine (as generated by thevending machine) and processes the images, video, and/or audio todetermine one or more states. For example, a ML model can process theimage and outputs one or more classifications, each classificationhaving a confidence score. For example, the ML model can output aclassification of a state—sober with a confidence score of 0.98. Theconfidence score represents a relative confidence in the accuracy of theclassification. In some examples, the confidence score can be comparedto a threshold confidence score. If the confidence score exceeds thethreshold confidence score, the classification is determined to becorrect. If the confidence score does not exceed the thresholdconfidence score, the classification is determined to be incorrect. Ifthe classification is determined to be correct, the state (e.g., sober)determined from the images, video, and/or audio, in the example above,is determined to be correct. If the classification is determined to beincorrect, the state (e.g., sober) determined from the images, video,and/or audio, in the example above, is determined to be incorrect.

As another example, a number of times that the consumer has been servedwithin a period of time (e.g., determined from the service history) canbe used to determine a state of the user. For example, a blood-alcoholcontent (BAC) value can be calculated based on the gender, weight (e.g.,data available to us from their ID document scan) and the number andtypes of beverages served. The BAC value can be compared to a thresholdBAC value. If the BAC value exceeds the threshold BAC value, service canbe denied.

In some implementations, one or more venue constraints, one or morelegal constraints, and one or more schedule constraints can be evaluatedto determine whether consumers are to be served. An example venueconstraint can include, without limitation, a time period during whichthe venue is offering beverages for sale. In some examples, if aconsumer requests a beverage outside of the time period, it can bedetermined that service of the beverage is to be declined. An examplelegal constraint can include, without limitation, a time period duringwhich sale of beverages are legally prohibited. In some examples, if aconsumer requests a beverage during the time period, it can bedetermined that service of the beverage is to be declined. An exampleschedule constraint can include, without limitation, a time periodduring which an event is occurring (e.g., a collegiate athletic event, areligious ceremony), and sales of beverages are legally and/orcontractually prohibited. In some examples, if a consumer requests abeverage during the time period, it can be determined that service ofthe beverage is to be declined.

With reference to FIG. 2B, another example architecture 200′ isprovided, in which verification of characteristics of a customer (e.g.,an age of a customer, an identity of a customer) is performed at a firstlocation, and retrieval of a good/service is performed at a secondlocation. In the example of FIG. 2B, a point-of-verification device 260is provided, which includes components of the vending machine 202 exceptfor the beverage dispensing system 228, and the I/O acquisition system230. Instead, in the example architecture 200′ of FIG. 2B, one or morebeverages dispensing systems 228 are provided as components ofrespective dispensing systems 262. In some examples, thepoint-of-verification device 260 is at a first location and executesverification of characteristics of a customer, as described herein. Insome examples, payment can be made at the first location using thepoint-of-verification device 260.

After verification (e.g., age and identity of the customer have beenverified), the customer can retrieve from one of the dispensing systems262, each located at respective second locations. In some examples, thepoint-of-verification device 260 informs the customer as to the specificdispensing system 262 that is to be used (e.g., Pick-up your beverage atMachine No. 456). This can occur when, for example, a particular good isonly available at one or more particular dispensing systems 262 (e.g.,Machine 123 dispenses lagers, Machine 456 dispenses stouts, Machine 789dispenses wheat beers). In some examples, the customer can select anydispensing system 262 of multiple dispensing systems 262.

In some implementations, the dispensing system 262 dispenses the good(e.g., beverage) in response to verifying the identity of the customerat the dispensing machine 262. For example, the dispensing machine 262can include one or more cameras (not depicted) that capture an image ofthe customer. In some examples, the image of the customer captured bythe dispensing machine 262 can be compared to one or more imagesprovided through the point-of-verification device 260 to confirm thatthe customer attempting to retrieve the good at the dispensing machine262 had been verified at the point-of-verification device. In someexamples, the dispensing system 262 can provide the images that are tobe compared to the facial recognition system 210, which can employ oneor more facial recognition models (e.g., ML models) that can compareimages to determine whether faces depicted in the images match, asdescribed herein. In the case where the

In some examples, in the case where the point-of-verification device 260informs the customer as to the specific dispensing system 262 that is tobe used, the point-of-verification device 260 can provide a transactionrecord including an image of the customer to the specific dispensingsystem 262. In this manner, the identity of the customer can be verifiedfor retrieval of the good at the dispensing system 262 based on theimage provided in the transaction record and the image captured by thedispensing system 262. In some examples, in the case where thepoint-of-verification device 260 informs the customer as to the specificdispensing system 262 that is to be used, the image captured by thedispensing system 262 is provided to the point-of-verification device260, which provides a confirmation as to whether the good should beprovided to the customer. For example, the point-of-verification device260 can determine (e.g., using the facial recognition system) whetherthe customer attempting to retrieve the good is the customer that wasverified at the point-of-verification device 260.

In some examples, in the case where, the customer can select anydispensing system 262 of multiple dispensing systems 262, thepoint-of-verification device 260 can provide a transaction recordincluding an image of the customer to each of the dispensing systems262. In this manner, the identity of the customer can be verified forretrieval of the good at the dispensing system 262 based on the imageprovided in the transaction record and the image captured by thedispensing system 262 the customer chooses to use. In some examples, inthe case where, the customer can select any dispensing system 262 ofmultiple dispensing systems 262, the image captured by the dispensingsystem 262 is provided to the point-of-verification device 260, whichprovides a confirmation as to whether the good should be provided to thecustomer. For example, the point-of-verification device 260 candetermine (e.g., using the facial recognition system) whether thecustomer attempting to retrieve the good is a customer that was verifiedat the point-of-verification device 260.

In some implementations, a good can be preemptively prepared forretrieval by a customer based on the customer's proximity to theretrieval location. For example, in the case of the example architecture200′ of FIG. 2B, which includes the point-of-verification device 260 andone or more dispensing systems 262, the good can be prepared forretrieval in response to determining that the customer is proximate to adispensing system 262. For example, images of faces of customers in lineto retrieve goods from the dispensing system 262 can be captured and itcan be determined that a customer has already been verified by thepoint-of-verification device 260. In response, the good (e.g., beverage)can be prepared (e.g., poured), such that, when the customer arrives atthe dispensing system 262, the good is available for immediate pick-up(e.g., the customer does not have to wait, or only has to wait arelatively short time to take the good). For example, the dispensingsystem 262 can evaluate customers Y deep in a line (e.g., 3 customersdeep in a line of customers) to continuously update a dispensing queuefor the goods. By the time the Y^(th) customer is at the dispensingsystem 262, the good for the particular customer is either alreadyprepared for pick-up (e.g., the beverage is already poured), orpreparation for pick-up is already underway (e.g., pouring of thebeverage has already begun).

In some implementations, a customer can place an order remotely beforeverification of characteristics of the customer. In some examples, acustomer can use a device (e.g., tablet, smartphone) that executes anapplication (e.g., mobile application (mobile app)) that enables thecustomer to place an order for a good that is to be retrieved. In someexamples, the customer is also able to pay for the order using theapplication. After ordering remotely, the customer can approach avending machine 202 of FIG. 2A, or a point-of-verification system 260 ofFIG. 2B for characteristic verification to be performed, as describedherein. In some examples, upon arriving at the vending machine 202 ofFIG. 2A, or the point-of-verification system 260 of FIG. 2B the customercan present evidence of the order, and the vending machine 202, or thepoint-of-verification system 260 can retrieve details of the order basedon the evidence. Example evidence can include, without limitation, amachine-readable code (e.g., a bar code, a QR code) that can begenerated by the application of the device of the customer and displayedto a camera and/or scanner of the vending machine 202, or thepoint-of-verification system 260. The vending machine 202, or thepoint-of-verification system 260 can decode the machine-readable code todetermine the details of the order, and commence verification of thecustomer, as described herein.

FIG. 3 depicts an example conceptual diagram 300 in accordance withimplementations of the present disclosure. The example conceptualdiagram 300 represents a so-called web of proof to determine whether aconsumer is to be served. The example conceptual diagram 300 includescircles indicating devices and/or data (e.g., IR camera still camera,video camera (which can be individual or combined cameras), ID scanner,know-your-client (KYC) data (e.g., answers providing personallyidentifiable information (PII)), and sets of rules to be applied) on thevending machine (e.g., the vending machine 100 of FIG. 1, the vendingmachine 202 of FIG. 2A). The example conceptual diagram 300 includesovals indicating data and/or sources of data that can be processed, asdescribed herein. The example conceptual diagram 300 includes diamondsindicating one or more determinations that are to be made to ultimatelydetermine whether a consumer is to be served.

In the example of FIG. 3, an example determination can be whether theconsumer is actually present at the vending machine (diamond→There?). Insome examples, this can be determined based on images and/or video fromthe still camera and the video camera, respectively (e.g., imagesdepicting liveliness, such as movement of a consumer present at thevending machine). Another example determination can be whether theconsumer as presented at the vending machine is real (diamond→Real?). Insome examples, this can be determined based on one or more depths(including a projected matrix depth (PMD) and liveliness. In thismanner, the vending machine cannot be spoofed by a consumer holding up apicture of another in front of the vending machine. In some examples,multiple depths are determined, and it is determined whether the depthscorrelate to one another (diamond→Corr'n (correlation)).

Although there is a multiplicity of each of devices and/or data(circles), data and/or sources of data (ovals), and determinations(diamonds), it is contemplated that sub-sets of each can be used inaccordance with implementations of the present disclosure. In someimplementations, all can be used. For example, whether a consumer is tobe served can be determined based on the identification being unexpired,the age indicated on the identification being of the legal age, and theconsumer matching the identification. As another example, whether aconsumer is to be served can be determined based on an observed consumerat the vending machine being real (e.g., not spoofed using an image of aperson; liveliness), the identification being unexpired, the ageindicated on the identification being of the legal age, and the consumermatching the identification. As another example, whether a consumer isto be served can be determined based on a state of the consumer at thevending machine (e.g., sober, drunk), the identification beingunexpired, the age indicated on the identification being of the legalage, and the consumer matching the identification. As another example,whether a consumer is to be served can be determined based on any venueconstraints, the identification being unexpired, the age indicated onthe identification being of the legal age, and the consumer matching theidentification. As another example, whether a consumer is to be servedcan be determined based on authenticity of the identification, theidentification being unexpired, the age indicated on the identificationbeing of the legal age, and the consumer matching the identification. Ingeneral, any appropriate combination of the above criteria fordetermining whether a consumer is to be served can be used.

FIGS. 4A-4F depict example user interfaces (UIs) in accordance withimplementations of the present disclosure. The example UIs can bedisplayed in the display screen 108 of the vending machine 100. FIG. 4Adepicts an example UI 400 depicting instructions for a consumer toinsert an identification. The example UI 400 includes a partial image ofthe vending machine (e.g., the vending machine 100 of FIG. 1), whichenables the consumer to orient themselves with respect to the vendingmachine. The example UI 400 also includes an arrow pointing to the IDscanner that is to be used to scan the identification. In this manner,the consumer is aware of which device to use to scan the identification.FIG. 4B depicts an example UI 402 depicting instructions for a consumerto have a photo (e.g., digital image) taken. The example UI 402 includesan image of the vending machine (e.g., the vending machine 100 of FIG.1), which enables the consumer to orient themselves with respect to thevending machine. The example UI 402 also includes an arrow pointing tothe camera that is to be used to capture the photo. In some examples,the UI 400 is displayed before the UI 402. In some examples, the UI 402is displayed before the UI 400. In some examples, one or more initialUIs are displayed prior to display of either the UI 400 or the UI 402.For example, an introduction UI and/or beverages selection UIs can bedisplayed prior to display of either the UI 400 or the UI 402.

FIG. 4C depicts an example UI 404 that provides a wait icon. The UI 404functions as a wait screen, while it is being determined whether theconsumer is to be served.

FIG. 4D depicts an example UI 406 that provides a result. In someexamples, the UI 406 informs the user that their identification has beenaccepted and the beverage will be served. In some examples, the UI 406is temporarily displayed and automatically changes to one or more UIsinstructing on the service process (e.g., when and where to insertvessel).

FIG. 4E depicts an example UI 408 to inform the consumer that theycannot be served. The UI 408 reflects a denial of service based oneither an invalid identification or an age that is not of the legal age.FIG. 4F depicts an example UI 410 to inform the consumer that theycannot be served. The UI 410 reflects a denial of service based on theconsumer not matching the identification (e.g., a determination that theconsumer is not authentic).

In some implementations, one or more UIs can present personalized menusbased on one or more of demographics of the customer and preferences ofthe customer. For example, and as described above, a demographic of thecustomer can be determined. In some examples, a menu displayed to thecustomer can be tailored based on the demographic. For example, for afirst demographic a first set of products is displayed, and, for asecond demographic, a second set of products is displayed. In someexamples, for the first demographic, the first set of products and thesecond set of products are both displayed, but the first set of productsis displayed more prominently than the second set of products (e.g.,toward the top of the UI with larger graphical representations). In someexamples, for the second demographic, the first set of products and thesecond set of products are both displayed, but the second set ofproducts is displayed more prominently than the first set of products(e.g., toward the top of the UI with larger graphical representations).

In some examples, the service history for a particular customer can beprovided, as described herein, a menu displayed to the customer can betailored based on the service history. For example, frequencies ofpurchases of respective goods can be determined, and more frequentlypurchased goods can be displayed more prominently than less frequentlypurchased goods. In some examples, if a frequency of purchase of a goodis below a threshold frequency, the good is not initially displayed tothe customer. For example, if the customer frequently purchases lagers,less frequently purchases stouts, and has never purchased a wheat beer,available lagers can be displayed more prominently in the UI thanavailable stouts, and available wheat beers are not initially displayed.

FIG. 5 depicts an example process 500 that can be executed inimplementations of the present disclosure. In some examples, the exampleprocess 500 is provided using one or more computer-executable programsexecuted by one or more computing devices.

Data is received (502). For example, data representative of theconsumer, who is present at the vending machine and is requestingservice can be received from the one or more cameras. As anotherexample, consumer-specific information and ID-specific information canbe received from an identification (e.g., scanned by an ID scanner). Itis determined whether the identification is expired (504). For example,an expiration data provided on the identification is compared to acurrent date. If the identification is expired, an error is displayed(506). For example, the error can indicate that the identification isexpired, and the consumer will not be served.

If the identification is not expired, it is determined whether ageprovided by the identification is of legal age (508). For example, theDOB provided on the identification is compared to a current data. If theage provided by the identification is not of legal age, an error isdisplayed (506). For example, the error can indicate that the consumeris not of legal age for service. If the age provided by theidentification is of legal age, it is determined whether theidentification is authentic (510). If the identification is notauthentic, an error is displayed (506). For example, the error canindicate that the identification is not acceptable, and the consumerwill not be served.

If the identification is authentic, it is determined whether theconsumer is authentic (512). If the consumer is not authentic, an erroris displayed (506). For example, the error can indicate that theconsumer does not match the identification, and the consumer will not beserved. If the consumer is authentic, it is determined whether any othercondition is present that would prohibit service of the beverage (514).For example, any schedule constraints, any venue constraints, and/or anylegal constraints can be evaluated to determine whether any othercondition is present that would prohibit service of the beverage. Ifanother condition is present that would prohibit service of thebeverage, an error is displayed (506). For example, the error canindicate the reason that service is declined (e.g., “There is currentlya collegiate sporting event taking place at the stadium, during which weare unable to serve alcohol.” or “It is Sunday morning, and we areunable to serve alcohol until 12 Noon.”).

If no other condition is present that would prohibit service of thebeverage, payment information is processed (516). It is determinedwhether the payment is approved (518). If the payment is not approved,an error is displayed (506). If the payment is approved, the beverageserving system is activated to serve the beverage (520).

While the example process 500 of FIG. 5 is comprehensive, it iscontemplated that the entirety of the example process 500 need not beperformed to determine whether to serve a consumer. For example, and asdescribed herein, any appropriate sub-set of determinations of theexample process 500 can be executed (e.g., whether a consumer is to beserved can be determined based on the identification being unexpired,the age indicated on the identification being of the legal age, and theconsumer matching the identification).

Implementations and all of the functional operations described in thisspecification may be realized in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Implementations may be realized asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium may be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “computing system” encompasses allapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, or multiple processorsor computers. The apparatus may include, in addition to hardware, codethat creates an execution environment for the computer program inquestion (e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or anyappropriate combination of one or more thereof). A propagated signal isan artificially generated signal (e.g., a machine-generated electrical,optical, or electromagnetic signal) that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) may be written in any appropriate form ofprogramming language, including compiled or interpreted languages, andit may be deployed in any appropriate form, including as a stand aloneprogram or as a module, component, subroutine, or other unit suitablefor use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program may bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program may be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry (e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit)).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any appropriate kind of digital computer.Generally, a processor will receive instructions and data from a readonly memory or a random access memory or both. Elements of a computercan include a processor for performing instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive data from ortransfer data to, or both, one or more mass storage devices for storingdata (e.g., magnetic, magneto optical disks, or optical disks). However,a computer need not have such devices. Moreover, a computer may beembedded in another device (e.g., a mobile telephone, a personal digitalassistant (PDA), a mobile audio player, a Global Positioning System(GPS) receiver). Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices (e.g., EPROM, EEPROM, and flash memory devices); magneticdisks (e.g., internal hard disks or removable disks); magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory may besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations may be realizedon a computer having a display device (e.g., a CRT (cathode ray tube),LCD (liquid crystal display) monitor) for displaying information to theuser and a keyboard and a pointing device (e.g., a mouse, a trackball, atouch-pad), by which the user may provide input to the computer. Otherkinds of devices may be used to provide for interaction with a user aswell; for example, feedback provided to the user may be any appropriateform of sensory feedback (e.g., visual feedback, auditory feedback,tactile feedback); and input from the user may be received in anyappropriate form, including acoustic, speech, or tactile input.

Implementations may be realized in a computing system that includes aback end component (e.g., as a data server), a middleware component(e.g., an application server), and/or a front end component (e.g., aclient computer having a graphical user interface or a Web browser,through which a user may interact with an implementation), or anyappropriate combination of one or more such back end, middleware, orfront end components. The components of the system may be interconnectedby any appropriate form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”) and a wide area network (“WAN”), e.g., theInternet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations may also beimplemented in combination in a single implementation. Conversely,various features that are described in the context of a singleimplementation may also be implemented in multiple implementationsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination may in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variation ofa sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemsmay generally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, various formsof the flows shown above may be used, with steps re-ordered, added, orremoved. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A computer-implemented method for selectivelyserving a consumer from a vending machine, the method comprising:receiving consumer-specific data and ID-specific data from anidentification presented by a consumer to a vending machine, theconsumer-specific data being absent personally identifiable information(PII), such that PII of the consumer is absent from storage within thevending machine and is absent from transmissions from the vendingmachine; processing at least a portion of the ID-specific data todetermine an expiration date of the identification; and determining thatthe identification is unexpired based on the expiration date, and inresponse: processing at least a portion of the ID-specific data todetermine a birthdate of an individual represented by theidentification, and determining that an age of the individual meets athreshold age, and in response: processing at least a portion of theID-specific data based on a type of the identification to determinewhether the identification is authentic, and serving the consumer fromthe vending machine at least partially in response to determining thatthe identification is authentic and determining that the consumer isauthentic relative to the identification.
 2. The method of claim 1,wherein determining whether the identification is authentic comprisescomparing features of the identification to known features of the typeof the identification.
 3. The method of claim 1, wherein determiningwhether the identification is authentic comprises providing an image ofthe identification to a machine learning (ML) model and receiving anindication from the ML, model indicating whether the identification isauthentic.
 4. The method of claim 1, wherein determining whether theconsumer is authentic relative to the identification comprises providingimage data and/or video data depicting the consumer and an image fromthe identification to a facial recognition system that processes thedata to determine whether the consumer matches the image provided withthe identification.
 5. The method of claim 1, further comprisingdetermining a state of the consumer, serving the consumer being furtherin response to the state of the user.
 6. The method of claim 1, furthercomprising determining that a person is physically present at thevending machine, serving the consumer being further in response todetermining that a person is physically present at the vending machine.7. The method of claim 6, wherein determining that a person isphysically present is at least partially based on a depth determinedusing one or more images generated by at least one camera of the vendingmachine.
 8. A system, comprising: a beverage dispenser; and acomputer-readable storage device coupled to the one or more processorsand having instructions stored thereon which, when executed by the oneor more processors, cause the one or more processors to performoperations for selectively serving a consumer from a vending machine,the operations comprising: receiving consumer-specific data andID-specific data from an identification presented by a consumer to avending machine, the consumer-specific data being absent personallyidentifiable information (PII), such that PII of the consumer is absentfrom storage within the vending machine and is absent from transmissionsfrom the vending machine; processing at least a portion of theID-specific data to determine an expiration date of the identification;and determining that the identification is unexpired based on theexpiration date, and at least partially in response: processing at leasta portion of the ID-specific data to determine a birthdate of anindividual represented by the identification, and determining that anage of the individual meets a threshold age, and in response: processingat least a portion of the ID-specific data based on a type of theidentification to determine whether the identification is authentic, andserving the consumer from the vending machine at least partially inresponse to determining that the identification is authentic anddetermining that the consumer is authentic relative to theidentification.
 9. The system of claim 8, wherein determining whetherthe identification is authentic comprises comparing features of theidentification to known features of the type of the identification. 10.The system of claim 8, wherein determining whether the identification isauthentic comprises providing an image of the identification to amachine learning (ML) model and receiving an indication from the ML,model indicating whether the identification is authentic.
 11. The systemof claim 8, wherein determining whether the consumer is authenticrelative to the identification comprises providing image data and/orvideo data depicting the consumer and an image from the identificationto a facial recognition system that processes the data to determinewhether the consumer matches the image provided with the identification.12. The system of claim 8, wherein operations further comprisedetermining a state of the consumer, serving the consumer being furtherin response to the state of the user.
 13. The system of claim 8, whereinoperations further comprise determining that a person is physicallypresent at the vending machine, serving the consumer being further inresponse to determining that a person is physically present at thevending machine.
 14. Non-transitory computer-readable storage mediacoupled to one or more processors and having instructions stored thereonwhich, when executed by the one or more processors, cause the one ormore processors to perform operations for selectively serving a consumerfrom a vending machine, the operations comprising: receivingconsumer-specific data and ID-specific data from an identificationpresented by a consumer to a vending machine, the consumer-specific databeing absent personally identifiable information (PII), such that PII ofthe consumer is absent from storage within the vending machine and isabsent from transmissions from the vending machine; processing at leasta portion of the ID-specific data to determine an expiration date of theidentification; and determining that the identification is unexpiredbased on the expiration date, and at least partially in response:processing at least a portion of the ID-specific data to determine abirthdate of an individual represented by the identification, anddetermining that an age of the individual meets a threshold age, and inresponse: processing at least a portion of the ID-specific data based ona type of the identification to determine whether the identification isauthentic, and serving the consumer from the vending machine at leastpartially in response to determining that the identification isauthentic and determining that the consumer is authentic relative to theidentification.
 15. The non-transitory computer-readable storage mediaof claim 14, wherein determining whether the identification is authenticcomprises comparing features of the identification to known features ofthe type of the identification.
 16. The non-transitory computer-readablestorage media of claim 14, wherein determining whether theidentification is authentic comprises providing an image of theidentification to a machine learning (ML) model and receiving anindication from the ML model indicating whether the identification isauthentic.
 17. The non-transitory computer-readable storage media ofclaim 14, wherein determining whether the consumer is authentic relativeto the identification comprises providing image data and/or video datadepicting the consumer and an image from the identification to a facialrecognition system that processes the data to determine whether theconsumer matches the image provided with the identification.